This project aims to develop and evaluate a coherent set of methods to understand behavior in complex information systems, such as the Internet, computational grids and computing clouds. Such large distributed systems exhibit global behavior arising from independent decisions made by many simultaneous actors, which adapt their behavior based on local measurements of system state. Actor adaptations shift the global system state, influencing subsequent measurements, leading to further adaptations. This continuous cycle of measurement and adaptation drives a time-varying global behavior. For this reason, proposed changes in actor decision algorithms must be examined at large spatiotemporal scale in order to predict system behavior. This presents a challenging problem.
What are complex systems? Large collections of interconnected components whose interactions lead to macroscopic behaviors in:
- Biological systems (e.g., slime molds, ant colonies, embryos)
- Physical systems (e.g., earthquakes, avalanches, forest fires)
- Social systems (e.g., transportation networks, cities, economies)
- Information systems (e.g., Internet and compute clouds)
What is the problem? No one understands how to measure, predict or control macroscopic behavior in complex information systems: (1) threatening our nation’s security and (2) costing billions of dollars.
“[Despite] society’s profound dependence on networks, fundamental knowledge about them is primitive. [G]lobal communication … networks have quite advanced technological implementations but their behavior under stress still cannot be predicted reliably.… There is no science today that offers the fundamental knowledge necessary to design large complex networks [so] that their behaviors can be predicted prior to building them.”
above quote from Network Science 2006, a National Research Council report
What is the new idea? Leverage models and mathematics from the physical sciences to define a systematic method to measure, understand, predict and control macroscopic behavior in the Internet and distributed software systems built on the Internet.
What are the technical objectives? Establish models and analysis methods that (1) are computationally tractable, (2) reveal macroscopic behavior and (3) establish causality. Characterize distributed control techniques, including: (1) economic mechanisms to elicit desired behaviors and (2) biological mechanisms to organize components.
Why is this hard? Valid computationally tractable models that exhibit macroscopic behavior and reveal causality are difficult to devise. Phase-transitions are difficult to predict and control.
Who would care? All designers and users of networks and distributed systems with a 25-year history of unexpected failures:
- ARPAnet congestion collapse of 1980
- Internet congestion collapse of Oct 1986
- Cascading failure of AT&T long-distance network in Jan 1990
- Collapse of AT&T frame-relay network in April 1998 …
Businesses and customers who rely on today's information systems:
- “Cost of eBay's 22-Hour Outage Put At $2 Million”, Ecommerce, Jun 1999
- “Last Week’s Internet Outages Cost $1.2 Billion”, Dave Murphy, Yankee Group, Feb 2000
- “…the Internet "basically collapsed" Monday”, Samuel Kessler, Symantec, Oct 2003
- “Network crashes…cost medium-sized businesses a full 1% of annual revenues”, Technology News, Mar 2006
- “costs to the U.S. economy…range…from $65.6 M for a 10-day [Internet] outage at an automobile parts plant to $404.76 M for … failure …at an oil refinery”, Dartmouth study, Jun 2006
Designers and users of tomorrow's information systems that will adopt dynamic adaptation as a design principle:
- DoD to spend $13 B over the next 5 yrs on Net-Centric Enterprise Services initiative, Government Computer News, 2005
- Market derived from Web services to reach $34 billion by 2010, IDC
- Grid computing market to exceed $12 billion in revenue by 2007, IDC
- Market for wireless sensor networks to reach $5.3 billion in 2010, ONWorld
- Revenue in mobile networks market will grow to $28 billion in 2011, Global Information, Inc.
- Market for service robots to reach $24 billion by 2010, International Federation of Robotics
Hard Issues & Plausible Approaches
|H1. Model scale
||A1. Scale-reduction techniques
|H2. Model validation
||A2. Sensitivity analysis & key comparisons
|H3. Tractable analysis
||A3. Cluster analysis and statistical analyses
|H4. Causal analysis
||A4. Evaluate analysis techniques
|H5. Controlling behavior
||A5. Evaluate distributed control regimes
Model scale – Systems of interest (e.g., Internet and compute grids) extend over large spatiotemporal extent, have global reach, consist of millions of components, and interact through many adaptive mechanisms over various timescales. Scale-reduction techniques must be employed. Which computational models can achieve sufficient spatiotemporal scaling properties? Micro-scale models are not computable at large spatiotemporal scale. Macro-scale models are computable and might exhibit global behavior, but can they reveal causality? Meso-scale models might exhibit global behavior and reveal causality, but are they computable? One plausible approach is to investigate abstract models from the physical sciences. e.g., fluid flows (from hydrodynamics), lattice automata (from gas chemistry), Boolean networks (from biology) and agent automata (from geography). We can apply parallel computing to scale to millions of components and days of simulated time. Scale reduction may also be achieved by adopting n-level experiments coupled for orthogonal fractional factorial (OFF) experiment designs.
Model validation – Scalable models from the physical sciences (e.g., differential equations, cellular automata, nk-Boolean nets) tend to be highly abstract. Can sufficient fidelity be obtained to convince domain experts of the value of insights gained from such abstract models? We can conduct sensitivity analyses to ensure the model exhibits relationships that match known relationships from other accepted models and empirical measurements. Sensitivity analysis also enables us to understand relationships between model parameters and responses. We can also conduct key comparisons along three complementary paths: (1) comparing model data against existing traffic and analysis, (2) comparing results from subsets of macro/meso-scale models against micro-scale models and (3) comparing simulations of distributed control regimes against results from implementations in test facilities, such as the Global Environment for Network Innovations.
Tractable analysis – The scale of potential measurement data is expected to be very large – O(10**15) – with millions of elements, tens of variables, and millions of seconds of simulated time. How can measurement data be analyzed tractably? We could use homogeneous models, which allow one (or a few) elements to be sampled as representative of all. This reduces data volume to 10**6 – 10**7, which is amenable to statistical analyses (e.g., power-spectral density, wavelets, entropy, Kolmogorov complexity) and to visualization. Where homogeneous models are inappropriate, we can use clustering analysis to view relationships among groups of responses. We can also exploit correlation analysis and principal components analysis to identify and exclude redundant responses from collected data. Finally, we can construct combinations of statistical tests and multidimensional data visualization techniques tailored to specific experiments and data of interest.
Causal analysis – Tractable analysis strategies yield coarse data with limited granularity of timescales, variables and spatial extents. Coarseness may reveal macroscopic behavior that is not explainable from the data. For example, an unexpected collapse in the probability density function of job completion times in a computing grid was unexplainable without more detailed data and analysis. Multidimensional analysis can represent system state as a multidimensional space and depict system dynamics through various projections (e.g., slicing, aggregation, scaling). State-space dynamics can segment system dynamics into an attractor-basin field and then monitor trajectories. Markov models providing compact, computationally efficient representations of system behavior can be subjected to perturbation analyses to identify potential failure modes and their causes.
Controlling Behavior – Large distributed systems and networks cannot be subjected to centralized control regimes because the system consists of too many elements, too many parameters, too much change, and too many policies. Can models and analysis methods be used to determine how well decentralized control regimes stimulate desirable system-wide behaviors? Use price feedback (e.g., auctions, present-value analysis or commodity markets) to modulate supply and demand for resources or services. Use biological processes to differentiate function based on environmental feedback, e.g., morphogen gradients, chemotaxis, local and lateral inhibition, polarity inversion, quorum sensing, energy exchange and reinforcement.
Additional Technical Details:
- K. Mills, "Understanding Behavior and Improving Reliability in Complex Information Systems", keynote presentation at the 4th PI meeeting for the DARPA Mission-Resilient Cloud program, Park Ridge, NJ, May 8-10, 2013.
- K. Mills, J. Filliben and C. Dabrowski, "Using Genetic Algorithms to Search for Failure Scenarios", poster presentation at Cloud Computing & Big Data Forum & Workshop, NIST, January 15-17, 2013.
- Mills, "Predicting the Unpredictable in Complex Information Systems", keynote presentation at the IEEE/ACM 5th International Conference on Utility & Cloud Computing, Chicago, Illinois, November 5-8, 2012.
- K. Mills, J. Filliben and C. Dabrowski, "Predicting Global Failure Regimes in Complex Information Systems", presentation at the DoE COMBINE Worskhop, Washington, DC, September 11-12, 2012.
- C. Dabrowski, J. Filliben, K. Mills, S. Ressler and B. Rust, "Mitigating Global Failure Regimes in Large Distributed Systems", poster presented at the Lawrence Livermore Workshop on Current Challenges in Computing 2012: Network Science, Napa, CA, August 28-29, 2012.
- A. Haines, "Determining Important Control Parameters of a Genetic Algorithm", Summery University Research Fellow Plenary Presentation, NIST, Gaithersburg, MD, August 7, 2012.
- C. Dabrowski, J. Filliben and K. Mills, "Predicting Global Failure Regimes in Complex Information Networks", Santa Fe Institute Workshop on Measurement of Complex Information Networks, Mitre, McLean, Virginia, July 12, 2012.
- C. Dabrowski, J. Filliben and K. Mills, "Predicting Global Failure Regimes in Complex Information Systems", NetONets 2012, Systemic Risk and Infrastructural Interdependencies, Northwestern University, June 19, 2012.
- K. Mills, J. Filliben and C. Dabrowski, "Improving Cloud Reliability", NIST Cloud Computing Forum & Workshop V, Department of Commerce, Washington, D.C., June 5-7, 2012.
- C. Dabrowski, J. Filliben, K. Mills, S. Ressler and B. Rust, "Poster on Mitigating Global Failure Regimes in Large Distributed Systems", presented at the NIST Cloud Computing Forum & Workshop V, Department of Commerce, Washington, D.C., June 5-7, 2012.
- K. Mills, J. Filliben and C. Dabrowski, "Comparing VM-Placement Algorithms for On-Demand Clouds", Large-Scale Networking Working Group, Arlington, VA, Feb. 14, 2012.
- C. Dabrowski and K. Mills, "VM Leakage & Orphan Control in Open-Source Clouds", IEEE CloudCom 2011, Athens, Dec. 1, 2011.
- K. Mills, J. Filliben and C. Dabrowski, "Comparing VM-Placement Algorithms for On-Demand Clouds", IEEE CloudCom 2011, Athens, Nov. 30, 2011.
- K. Mills, C. Dabrowski, J. Filliben and F. Hunt, "Posters Presented at NIST Cloud Computing Forum & Workshop IV", Gaithersburg, MD, Nov. 3-4, 2011.
- J. Filliben and K. Mills, "Comparison of Two Dimension-Reduction Methods for Network Simulation Models", Statistical Engineering Division Seminar, NIST, Gaithersburg, MD, Sept. 22, 2011.
- C. Dabrowski and F. Hunt, "Using Markov Chain and Graph Theory Concepts to Analyze Behavior in Complex Distributed Systems", 23rd European Modeling and Simulation Symposium, Rome, Sept. 13, 2011.
- K. Mills, J. Filliben, D.-Y. Cho and E. Schwartz, "Predicting Macroscopic Dynamics in Large Distributed Systems", American Society of Mechanical Engineers 2011 Conference on Pressure Vessels & Piping, Baltimore, MD, July 21, 2011.
- C Dabrowski and F. Hunt, "Identifying Failure Scenarios in Complex Systems by Perturbing Markov Chain Models", American Society of Mechanical Engineers 2011 Conference on Pressure Vessels & Piping, Baltimore, MD, July 21, 2011.
- K. Mills, J. Filliben and C. Dabrowski, "An Efficient Sensitivity Analysis Method for Large Cloud Simulations", IEEE Cloud 2011, Washington, D.C., July 8, 2011.
- K. Mills, J. Filliben, D.-Y. Cho and E. Schwartz, "Predicting Macroscopic Dynamics in Large Distributed Systems", LSN Seminar on Complex Networks and Information Systems, Gaithersburg, Maryland, June 30, 2011.
- K. Mills, J. Filliben, C. Dabrowski and S. Ressler, "Posters Presented NIST Work on Measurement Science for Complex Systems, as Applied to Cloud Computing Systems", NIST Cloud Computing Forum & Workshop III, Gaithersburg, Maryland, April 7-8, 2011.
- K. Mills, E. Schwartz and J. Yuan, "How to Model a TCP/IP Network using only 20 Parameters", Winter Simulation Conference (WSC 2010), Baltimore, Maryland, Dec. 8, 2010.
- K. Mills and J. Filliben, "Using Sensitivity Analysis to Identify Significant Parameters in a Network Simulation", Winter Simulation Conference (WSC 2010), Baltimore, Maryland, Dec. 6, 2010.
- K. Mills and J. Filliben, "Comparing Two Dimension-Reduction Methods for Network Simulation Models", Winter Simulation Conference (WSC 2010), Baltimore, Maryland, Dec. 6, 2010.
- K. Mills, "Study of Proposed Internet Congestion Control Algorithms", Internet Congestion Control Research Group (ICCRG) of the Internet Research Task Force (IRTF) at the 77th Internet Engineering Task Force (IETF) meeting at Anaheim, California, March 24, 2010.
- K. Mills and J. Filliben, "An Efficient Sensitivity Analysis Method for Mesoscopic Network Models", Complex Systems Study Group, NIST, February 2, 2010
- K. Mills, "Study of Proposed Internet Congestion Control Algorithms", seminar sponsored by the Computer Science Department and the C4I Center at George Mason University, Fairfax, Virginia, January 29, 2010.
- K. Mills, "How to model a TCP/IP network using on 20 parameters", Complex Systems Study Group, NIST, November 17, 2009
- K. Mills, "Measurement Science for Complex Information Systems", invited presentation to the Internet Congestion-Control Research Group (ICC-RG) of the Internet Research Task Force (IRTF) at Tokyo, Japan, May 20, 2009.
- K. Mills, "Measurement Science for Complex Information Systems", seminar sponsored by the Computer Science Department and the C4I Center at George Mason University, Fairfax, Virginia, March 27, 2009.
- K. Mills, "Measurement Science for Complex Information Systems", AOL Network Architecture Group, Dulles, Virginia, March 18, 2009.
- K. Mills, "Measurement Science for Complex Information Systems", NITRD Large-Scale Networking Working Group, Ballston, Virginia, March 10, 2009.
- K. Mills, "Progress Report on Measurement Science for Complex Information Systems", Complex Systems Lecture Series, NIST Information Technology Laboratory, Gaithersburg, Maryland, January 27, 2009.
- J. Filliben, "Sensitivity Analysis Methodology for a Complex System Computational Model", 39th Symposium on the Interface: Computing Science and Statistics, Philadelphia, PA, May 26, 2007.
- C. Dabrowski and K. Mills, "A Program of Work for Understanding Emergent Behavior in Global Grid Systems", Global Grid Forum 16, Athens, Greece, February 13, 2006.
Nov 2011 In the fall of 2009, this project started investigating large scale behavior in Infrastructure Clouds. The project produced three related papers during 2011, and all three papers were accepted at the two major IEEE cloud computing conferences held during the year. The rapid success of the project in this new domain illustrates the general applicability of the methods we developed, as well as the ease with which those methods can be applied.
Nov 2010 Developed and demonstrated Koala, a discrete-event simulator for Infrastructure Clouds. Completed a sensitivity analysis of Koala to identify unique response dimensions and significant factors driving model behavior. Created multidimensional animations to visualize spatiotemporal variation in resource usage and load for cores, disks, memory and network interfaces in clouds with up to O(10**5) nodes.
May 2010 NIST Special Publication 500-282: Study of Proposed Internet Congestion Control Mechanisms
Sep 2009 Draft NIST Special Publication: Study of Proposed Internet Congestion-Control Mechanisms
Apr 2009 Demonstrated applicability of Markov model perturbation analysis to communication networks.
Sep 2008 Developed a Markov model for a global, computational grid and demonstrated the feasibility of applying perturbation analysis to predict conditions that could lead to performance degradation. Currently, perturbation analysis is a theoretical topic for which we show applications to large distributed systems.
Aug 2008 Developed and demonstrated multidimensional visualization software to explore relationships among complex data sets derived from simulations of large distributed systems. Currently, there are no widely used visualization techniques to explore multidimensional data from simulations of large distributed systems.
Jun 2008 Developed and demonstrated an analytical framework to understand relationships among pricing, admission control and scheduling for resource allocation in computing clusters. Currently, resource-allocation mechanisms for computing clusters rely on heuristics.
Apr 2008 Developed and validated MesoNetHS, which adds six proposed replacement congestion-control algorithms to MesoNet and allows the behavior of the algorithms to be investigated in a large topology. Currently, these congestion-control algorithms are explored in simulated and empirical topologies of small size.
Sep 2007 Developed and demonstrated a methodology for sensitivity analysis of models of large distributed systems. Currently, sensitivity analysis of models for large distributed systems is considered infeasible.
Apr 2007 Developed and verified MesoNet, a mesoscopic scale network simulation model that can be specified with about 20 parameters. Currently, specifying most network simulations requires hundreds to thousands of parameters.
October 2, 2006
Lead Organizational Unit:
Dong Yeon Cho
Related Programs and Projects:
- C. Dabrowski, F. Hunt and K. Morrison, Improving the Efficiency of Markov Chain Analysis of Complex Distributed Systems, NIST Inter-Agency Report 7744, November 2010.
- K. Mills, E. Schwartz and J. Yuan, "How to Model a TCP/IP Network using only 20 Parameters", Proceedings of the 2010 Winter Simulation Conference (WSC 2010), Dec. 5-8, Baltimore, MD.
- K. Mills and J. Filliben, "An Efficient Sensitivity Analysis Method for Network Simulation Models", presented at the 2010 Winter Simulation Conference (WSC 2010), Dec. 5-8, Baltimore, MD.
- K. Mills, J. Filliben, D. Cho, E. Schwartz and D. Genin, Study of Proposed Internet Congestion Control Mechanisms, NIST Special Publication 500-282, May 2010, 534 pages.
- D. Genin and V. Marbukh, "Bursty Fluid Approximation of TCP for Modeling Internet Congestion at the Flow Level", Proceedings of the 47th Annual Allerton Conference on Communication, Control, and Computing, Sept 30-Oct 2, 2009.
- V. Marbukh, “From Network Microeconomics to Network Infrastructure Emergence”, Proceedings of the 1st IEEE International Workshop on Network Science for Communication Networks (NetSciCom 2009), held in conjunction with IEEE Infocom 2009, April 24, 2009 - Rio de Janeiro, Brazil.
- F. Hunt and V. Marbukh, "Measuring the Utility/Path Diversity Tradeoff in Multipath Protocols", Proceedings of the 4th International Conference on Performance Evaluation Methodologies and Tools, Pisa, Italy, October 20-22, 2009.
- C. Dabrowski, “Reliability in grid computing systems”, in Concurrency and Computation: Practice and Experience, John Wiley & Sons, 21/8, pp. 927-959, 2009.
- C. Dabrowksi and F. Hunt, “Using Markov Chain Analysis to Study Dynamic Behaviour in Large-Scale Grid Systems”, Proceedings of the 7th Australasian Symposium on Grid Computing and e-Research, Wellington, New Zealand, Jan. 2009.
- C. Dabrowski and F. Hunt, Markov Chain Analysis for Large-Scale Grid Systems, NIST Inter-Agency Report 7566, January 2009.
- D. Genin and V. Marbukh, "Toward Understanding of Metastability in Cellular CDMA Networks: Emergence and Implications for Performance." GLOBECOM 2008, New Orleans, Nov. 31 - Dec. 4.
- K. Mills and C. Dabrowski, “Can Economics-based Resource Allocation Prove Effective in a Computation Marketplace?", Journal of Grid Computing, 6/3, September 2008, pp. 291-311.
- F. Hunt and V. Marbukh, “Dynamic Routing and Congestion Control Through Random Assignment of Routes”, Proceedings of the 5th International Conference on Cybernetics and Information Technologies, Systems and Applications: CITSA 2008, Orlando FL, July 2008. (BEST PAPER)
- V. Marbukh, "Can TCP Metastability Explain Cascading Failures and Justify Flow Admission Control in the Internet?", Proceedings of the 15th International Conference on Telecommunications, Saint Peterbsurg, Russia, June 16-19, 2008.
- V. Marbukh and K. Mills, "Demand Pricing & Resource Allocation in Market-based Compute Grids: A Model and Initial Results", Proceedings of the 7th International Conference on Networking, IEEE, April 2008, pp. 752-757.
- V. Marbukh and S. Klink, "Decentralized Control of Large-Scale Networks as a Game with Local Interactions: Cross-Layer TCP/IP Optimization", 2nd International Conference on Performance Evaluation Methodologies and Tools, Nantes, France, October 23-25, 2007.
- V. Marbukh, "Utility Maximization for Resolving Throughput/Reliability Trade-offs in an Unreliable Network with Multipath Routing", 2nd International Conference on Performance Evaluation Methodologies and Tools, Nantes, France, October 23-25, 2007.
- V. Marbukh, "Fair Bandwidth Sharing under Flows Arrivals/Departures: Effect of Retransmissions on Stability and Performance", ACM Sigmetrics Performance Evaluation Review, Vol. 35, No. 2, pp. 6-8.
- V. Marbukh, "Metastability of fair bandwidth sharing under fluctuating demand and necessity of admission control", IEE Electronics Letters, Vol. 43, No. 19. pp. 1051-1053.
- V. Marbukh and K. Mills, "On Maximizing Provider Revenue in Market-Based Compute Grids", Proceedings of the 3rd International Conference on Networking and Services, Athens, Greece, June 19-25, 2007.
- K. Mills, "A Brief Survey of Self-Organization in Wireless Sensor Networks", Wireless Communications and Mobile Computing, Wiley Interscience, 7/7, September 2007, pp. 823-834.
- K. Mills and C. Dabrowski, "Investigating Global Behavior in Computing Grids", Self-Organizing Systems, Lecture Notes in Computer Science, Volume 4124 ISBN 978-3-540-37658-3, pp. 120-136.
- K. Sriram, D. Montgomery, O. Borchert, O. Kim and D. R. Kuhn, "Study of BGP Peering Session Attacks and Their Impacts on Routing Performance", IEEE Journal on Selected Areas in Communications, 24/10, October 2006, pp. 1901-1915.
- J. Yuan and K. Mills, "Simulating Timescale Dynamics of Network Traffic Using Homogeneous Modeling", The NIST Journal of Research, 111/3, May-June 2006, pp. 227-242.
- J. Yuan and K. Mills, "Monitoring the Macroscopic Effect of DDoS Flooding Attacks", IEEE Transactions on Dependable and Secure Computing, 2/4, October-December 2005, pp. 324-335.
- J. Yuan and K. Mills, "A Cross-Correlation Based Method for Spatial-Temporal Traffic Analysis", Performance Evaluation, 61/2-3, pp 163-180.
- J. Yuan and K. Mills, "Macroscopic Dynamics in Large-Scale Data Networks", chapter 8 in Complex Dynamics in Communication Networks, edited by Ljupco Kocarev and Gabor Vattay, published by Springer, 2005, ISBN 3-540-24305-4, pp. 191-212.
- J. Yuan and K. Mills, "Exploring Collective Dynamics in Communication Networks", The NIST Journal of Research, 107/2, March-April 2002, pp. 179-191.
- J. Heidemann, K. Mills and S. Kumar, "Expanding Confidence in Network Simulation", IEEE Network Magazine, 15/5, September/October 2001, pp. 58-63.
Related Software Tools
- SLX software for simulated computing grid used in "Investigating Global Behavior in Computing Grids". (see http://www.wolverinesoftware.com/ for information on SLX)
- Matlab MFiles used in "Simulating Timescale Dynamics of Network Traffic Using Homogeneous Modeling".(see http://www.mathworks.com/ for information on Matlab)
- Matlab MFiles used in "Monitoring the Macroscopic Effect of DDoS Flooding Attacks".
- Matlab MFiles used in "A Cross-Correlation Based Method for Spatial-Temporal Traffic Analysis".
- Matlab MFiles used in "Macroscopic Dynamics in Large-Scale Data Networks".
- Matlab MFiles used in "Exploring Collective Dynamics in Communication Networks".
- MesoNet: a Medium-scale Simulation Model of a Router-Level Internet-like Network
- EconoGrid: a detailed Simulation Model of a Standards-based Grid Compute Economy
- Flexi-Cluster: a Simulator for a Single Compute Cluster
- MesoNetHS: A Medium-scale Network Simulation with TCP Congestion-Control Algorithms for High Speed Networks, including BIC, Compound TCP, FAST, H- TCP, HS-TCP and Scalable TCP
- Divisa: software for interactive visualization of multidimensional data
- Markov Model Rewriter: A Discrete Time Markov chain simulation and perturbation system
- Koala: a medium-scale discrete-event simulation of Infrastructure Clouds, including various algorithms for allocating virtual machines to clusters and nodes.
- Animation (176 Mbyte Quicktime Movie) of vCore, Memory and Disk Space usage and pCore, Disk Count and NIC Count load from a Koala Simulation (Oct. 22, 2010) of a 20 cluster x 200 node (i.e., 4,000 node) Infrastructure Cloud evolving over 1200 hours.
- Visualization (10 Mbyte .avi) from a Simulation (May 23, 2007) of an Abilene-style Network
- Visualization (14.4 Mbyte .avi) from a Simulation (July 31, 2007) of a Network Running CTCP